According to Gartner, Composite AI refers to “the combined application (or fusion) of different AI techniques to improve the efficiency of learning to broaden the level of knowledge representations.” Garter explains that Composite AI provides a platform to solve a broader range of business problems more effectively through what are known as “AI abstraction mechanisms.”
Another way to frame it – perhaps one that’s a bit simpler – is this: Composite AI, as an umbrella-esque term, combines different (and often abstract) approaches to harnessing artificial intelligence to achieve better business results. In this case, “different approaches” include machine learning (ML), generative AI (GenAI), predictive utilizations of AI, natural language processing (NLP), data mining, computer vision, and many others. It basically depends on what business problem requires solving. (Another helpful breakdown of Composite AI, courtesy of SAS, can be read here.)
Recently, Dynatrace – with teams that combine deep observability, AIOps, and application security in a unified and precise intelligent data platform – released the official findings of an independent survey of approximately 1,300 CTOs and CIOs (amongst other technology leaders). This research detailed organizations’ increased investments in artificial intelligence; investments aimed at boosting productivity, automating routine tasks, reducing operational costs, and keeping pace with 1.) the competition of today, and 2.) the deep proliferation of AI, in general.
Composite AI, in this vein, was covered quite a bit and was juxtaposed with both the benefits and challenges that must be managed and overcome in order to truly support business-critical use cases in 2024 and beyond.
Here are some of the long-story-short findings and other datapoints that Dynatrace pulled together.
From the surveyed technology leaders and their organizations:
According to Dynatrace’s own CTO, Bernd Greifeneder:
“AI has become central to how organizations drive efficiency, improve productivity, and accelerate innovation. The release of ChatGPT late last year triggered a significant GenAI hype cycle. Business, development, operations, and security leaders have set high expectations for GenAI to help them deliver new services with less effort and at record speeds. However, as organizations endeavor to realize the expected value, it becomes evident that GenAI requires domain-specific tuning and integration with other technologies, including other types of AI. In addition, organizations must use AI securely and responsibly and monitor it closely to manage cost and user experience. This will help them provide accurate results, reduce expenses, and prevent employees from exposing sensitive data or creating vulnerabilities in their environments.”
Future of Work Contributor
Intapp, a provider of AI-powered software for professionals in specialized industries, announced new features and a refreshed brand focused on its "In…
Akto recently launched its GenAI Security Testing solution to offer proactive security testing specifically designed for GenAI models and their APIs.
Quantive's new StrategyAI solution hones organizations' efforts for developing, executing, and evaluating smarter, "always-on" business strategies.
The Gen AI fabric platform aims to simplify and accelerate the development and implementation of GenAI applications, which lets businesses harness the…
Eric Vaughan delivered an in-depth keynote presentation on the latest technology revolution, catalyzed by artificial intelligence (AI).